Visual tracking via improving motion model and model updater

Abstract

Motion model and model updater are two necessary components for online visual tracking. On the one hand, an effective motion model needs to strike the right balance between target processing, to account for the target appearance and scene analysis, and to describe stable background information. Most conventional trackers focus on one aspect out of the two and hence are not able to achieve the correct balance. On the other hand, the admirable model update needs to consider both the tracking speed and the model drift. Most tracking models are updated on every frame or fixed frames, so it cannot achieve the best performance. In this article, we solve the motion model problem by collaboratively using salient region detection and image segmentation. Particularly, the two methods are for different purposes. In the absence of prior knowledge, the former considers image attributes like color, gradient, edges, and boundaries then forms a robust object; the latter aggregates individual pixels into meaningful atomic regions by using the prior knowledge of target and background in the video sequence. Taking advantage of their complementary roles, we construct a more reasonable confidence map. For model update problems, we dynamically update the model by analyzing scene with image similarity, which not only reduces the update frequency of the model but also suppresses the model drift. Finally, we use these improved building blocks not only to do comparative tests but also to give a basic tracker, and extensive experimental results on OTB50 show that the proposed methods perform favorably against the state-of-the-art methods.